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Time, Space Gene Ontology, Glycomics, Proteomics Pharma Drug, Treatment-Diagnosis Repertoire Management Equity Markets Anti-money Laundering, Financial Risk, Terrorism Biomedicine is one of the most popular domains in which lots of ontologies have been developed and are in use. See: Clinical/medical domain is also a popular domain for ontology development and applications: Extensive work in creating Ontologies

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11 Semantic Ambiguity in Entity Extraction NCI NCI|nci|128|1|v|1|128|1|n|0|3| NCI|nCi's|128|8|v|1|128|1|b+i|2|3| NCI|nCis|128|8|v|1|128|1|b+i|2|3| NCI|National Cancer Institute|128|1|v|1|128|1|b+a|3|1| NCI|nanocurie|128|1|v|1|128|1|b+a|3|1| NCI|nanocuries|128|8|v|1|128|1|b+a+i|4|1| The ambiguity could be resolved though various techniques such as co-reference resolution or evidence based matching, or modeled using probability that the term represents any of the distinct (known) entities.

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12 Semantic Web application demonstration 1 Insider Threat: an example Semantic Web application that consists of (a) an ontology populated from multiple knowledge sources with heterogeneous representation formats, (b) ontology-supported entity extraction/annotation, (c) computation of semantic associations/relationships to terms in metadata with a (semantic) query represented in terms of ontology and the entities identified in the documents, (d) ranking of documents based on the strength of these semantic associations/relationships Demo of Ontological Approach to Assessing Intelligence Analyst Need-to-Know

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18 Relationship Web Semantic Metadata can be extracted from unstructured (eg, biomedical literature), semi-structured (eg, some of the Web content), structured (eg, databases) data and data of various modalities (eg, sensor data, biomedical experimental data). Focusing on the relationships and the web of their interconnections over entities and facts (knowledge) implicit in data leads to a Relationship Web. Relationship Web takes you away from which document could have information I need, to whats in the resources that gives me the insight and knowledge I need for decision making. Amit P. ShethAmit P. Sheth, Cartic Ramakrishnan: Relationship Web: Blazing Semantic Trails between Web Resources. IEEE Internet Computing, July 2007.Cartic RamakrishnanRelationship Web: Blazing Semantic Trails between Web Resources. IEEE Internet Computing, July 2007

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19 Prototype Semantic Web application demonstration 2 Demonstration of Semantic Trailblazing using a Semantic Browser This application demonstrating use of ontology-supported relationship extraction (represented in RDF) and their traversal in context (as deemed relevant by the scientists), linking parts of knowledge represented in one biomedical document (currently a sentence in an abstract in Pubmed) to parts of knowledge represented in another document. This is a prototype and lot more work remains to be done to build a robust system that can support Semantic Trailblazing. For more information: Cartic RamakrishnanCartic Ramakrishnan, Krys Kochut, Amit P. Sheth: A Framework for Schema-Driven Relationship Discovery from Unstructured Text. International Semantic Web Conference 2006: [.pdf]Krys KochutAmit P. ShethInternational Semantic Web Conference 2006[.pdf] Cartic RamakrishnanCartic Ramakrishnan, Amit P. Sheth: Blazing Semantic Trails in Text: Extracting Complex Relationships from Biomedical Literature. Tech. Report #TR-RS2007 [.pdf]Amit P. Sheth[.pdf]

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Approaches for Weighted Graphs QUESTION 1: Given an RDF graph without weights can we use domain knowledge to compute the strength of connection between any two entities? QUESTION 2: Can we then compute the most relevant connections for a given pair of entities? QUESTION 3: How many such connections can there be? Will this lead to a combinatorial explosion? Can the notion of relevance help?

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23 Class and Property Specificity (CS, PS) More specific classes and properties convey more information Specificity of property p i : –d(p i ) is the depth of p i –d(p iH ) is the depth of the property hierarchy Specificity of class c j : –d(c i ) is the depth of c j –d(c iH ) is the depth of the class hierarchy Node is weighted and this weight is propagated to edges incident to the node

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35 Summary We discuss some scenarios tying evidence based reasoning and the need to add representations and reasoning that involve approximate information in the context of current research in Semantic Web Knowledge enable Information & Services Science Center: